# WACV-2024-Papers

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## Adversarial Learning, Adversarial Attack and Defense Methods

![Section Papers](https://img.shields.io/badge/Section%20Papers-22-42BA16) ![Preprint Papers](https://img.shields.io/badge/Preprint%20Papers-15-b31b1b) ![Papers with Open Code](https://img.shields.io/badge/Papers%20with%20Open%20Code-10-1D7FBF) ![Papers with Video](https://img.shields.io/badge/Papers%20with%20Video-20-FF0000)

| **Title** | **Repo** | **Paper** | **Video** |
|-----------|:--------:|:---------:|:---------:|
| [Adversarial Likelihood Estimation with One-Way Flows](https://openaccess.thecvf.com/content/WACV2024/html/Ben-Dov_Adversarial_Likelihood_Estimation_With_One-Way_Flows_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Ben-Dov_Adversarial_Likelihood_Estimation_With_One-Way_Flows_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2307.09882-b31b1b.svg)](http://arxiv.org/abs/2307.09882) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=jU_GXTOJO8Q) |
| [NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations](https://openaccess.thecvf.com/content/WACV2024/html/Cha_NCIS_Neural_Contextual_Iterative_Smoothing_for_Purifying_Adversarial_Perturbations_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Cha_NCIS_Neural_Contextual_Iterative_Smoothing_for_Purifying_Adversarial_Perturbations_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2106.11644-b31b1b.svg)](http://arxiv.org/abs/2106.11644) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=cVPKwyjA-3M) |
| [On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization](https://openaccess.thecvf.com/content/WACV2024/html/Mehra_On_the_Fly_Neural_Style_Smoothing_for_Risk-Averse_Domain_Generalization_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/akshaymehra24/RiskAverseDG?style=flat)](https://github.com/akshaymehra24/RiskAverseDG) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Mehra_On_the_Fly_Neural_Style_Smoothing_for_Risk-Averse_Domain_Generalization_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2307.08551-b31b1b.svg)](http://arxiv.org/abs/2307.08551) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=1wtAijdPNuU) |
| [D4: Detection of Adversarial Diffusion Deepfakes using Disjoint Ensembles](https://openaccess.thecvf.com/content/WACV2024/html/Hooda_D4_Detection_of_Adversarial_Diffusion_Deepfakes_Using_Disjoint_Ensembles_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Hooda_D4_Detection_of_Adversarial_Diffusion_Deepfakes_Using_Disjoint_Ensembles_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2202.05687-b31b1b.svg)](http://arxiv.org/abs/2202.05687) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=QCOGaaUVoqs) |
| [Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based Sample Selection](https://openaccess.thecvf.com/content/WACV2024/html/Jindal_Army_of_Thieves_Enhancing_Black-Box_Model_Extraction_via_Ensemble_Based_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/akshitjindal1/AOT_WACV?style=flat)](https://github.com/akshitjindal1/AOT_WACV) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Jindal_Army_of_Thieves_Enhancing_Black-Box_Model_Extraction_via_Ensemble_Based_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2311.04588-b31b1b.svg)](http://arxiv.org/abs/2311.04588) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=lmUnWIryAM8) |
| [Assist is Just as Important as the Goal: Image Resurfacing to aid Model's Robust Prediction](https://openaccess.thecvf.com/content/WACV2024/html/Sharma_Assist_Is_Just_As_Important_as_the_Goal_Image_Resurfacing_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Sharma_Assist_Is_Just_As_Important_as_the_Goal_Image_Resurfacing_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2311.01563-b31b1b.svg)](http://arxiv.org/abs/2311.01563) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=JI-DhsAd9XM) |
| [CLIPAG: Towards Generator-Free Text-to-Image Generation](https://openaccess.thecvf.com/content/WACV2024/html/Ganz_CLIPAG_Towards_Generator-Free_Text-to-Image_Generation_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/royg27/CLIPAG?style=flat)](https://github.com/royg27/CLIPAG) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Ganz_CLIPAG_Towards_Generator-Free_Text-to-Image_Generation_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2306.16805-b31b1b.svg)](http://arxiv.org/abs/2306.16805) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=uqiJHx1tG3g) |
| [Defending Object Detection Models Against Image Distortions](https://openaccess.thecvf.com/content/WACV2024/html/Ofori-Oduro_Defending_Object_Detection_Models_Against_Image_Distortions_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/moforio/GSES?style=flat)](https://github.com/moforio/GSES) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Ofori-Oduro_Defending_Object_Detection_Models_Against_Image_Distortions_WACV_2024_paper.pdf) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=0I3gLAqgvHU) |
| [ATS: Adaptive Temperature Scaling for Enhancing Out-of-Distribution Detection Methods](https://openaccess.thecvf.com/content/WACV2024/html/Krumpl_ATS_Adaptive_Temperature_Scaling_for_Enhancing_Out-of-Distribution_Detection_Methods_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Krumpl_ATS_Adaptive_Temperature_Scaling_for_Enhancing_Out-of-Distribution_Detection_Methods_WACV_2024_paper.pdf) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=MiYVd2utfVs) |
| [A Closer Look at Robustness of Vision Transformers to Backdoor Attacks](https://openaccess.thecvf.com/content/WACV2024/html/Subramanya_A_Closer_Look_at_Robustness_of_Vision_Transformers_to_Backdoor_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/UCDvision/backdoor_transformer?style=flat)](https://github.com/UCDvision/backdoor_transformer) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Subramanya_A_Closer_Look_at_Robustness_of_Vision_Transformers_to_Backdoor_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2206.08477-b31b1b.svg)](http://arxiv.org/abs/2206.08477) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=mwqvn-Nqzbg) |
| [Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning](https://openaccess.thecvf.com/content/WACV2024/html/Wang_Maximum_Knowledge_Orthogonality_Reconstruction_With_Gradients_in_Federated_Learning_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/wfwf10/MKOR?style=flat)](https://github.com/wfwf10/MKOR) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Wang_Maximum_Knowledge_Orthogonality_Reconstruction_With_Gradients_in_Federated_Learning_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2310.19222-b31b1b.svg)](http://arxiv.org/abs/2310.19222) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=PYnxVjmA7cc) |
| [Learning to Generate Training Datasets for Robust Semantic Segmentation](https://openaccess.thecvf.com/content/WACV2024/html/Hariat_Learning_To_Generate_Training_Datasets_for_Robust_Semantic_Segmentation_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Hariat_Learning_To_Generate_Training_Datasets_for_Robust_Semantic_Segmentation_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2308.02535-b31b1b.svg)](http://arxiv.org/abs/2308.02535) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=CbrIWn3BpEA) |
| [Uncertainty-Weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation](https://openaccess.thecvf.com/content/WACV2024/html/Maag_Uncertainty-Weighted_Loss_Functions_for_Improved_Adversarial_Attacks_on_Semantic_Segmentation_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/kmaag/Uncertainty-weighted-Loss?style=flat)](https://github.com/kmaag/Uncertainty-weighted-Loss) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Maag_Uncertainty-Weighted_Loss_Functions_for_Improved_Adversarial_Attacks_on_Semantic_Segmentation_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2310.17436-b31b1b.svg)](http://arxiv.org/abs/2310.17436) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=mCWl5M79Y0Q) |
| [Natural Light can also be Dangerous: Traffic Sign Misinterpretation Under Adversarial Natural Light Attacks](https://openaccess.thecvf.com/content/WACV2024/html/Hsiao_Natural_Light_Can_Also_Be_Dangerous_Traffic_Sign_Misinterpretation_Under_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/BlueDyee/natural-light-attack?style=flat)](https://github.com/BlueDyee/natural-light-attack) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Hsiao_Natural_Light_Can_Also_Be_Dangerous_Traffic_Sign_Misinterpretation_Under_WACV_2024_paper.pdf) | :heavy_minus_sign: |
| [Diffusion Models Meet Image Counter-Forensics](https://openaccess.thecvf.com/content/WACV2024/html/Tailanian_Diffusion_Models_Meet_Image_Counter-Forensics_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/mtailanian/diff-cf?style=flat)](https://github.com/mtailanian/diff-cf) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Tailanian_Diffusion_Models_Meet_Image_Counter-Forensics_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2311.13629-b31b1b.svg)](http://arxiv.org/abs/2311.13629) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=Q8iN-hzuCjQ) |
| [Discriminator-Free Unsupervised Domain Adaptation for Multi-Label Image Classification](https://openaccess.thecvf.com/content/WACV2024/html/Singh_Discriminator-Free_Unsupervised_Domain_Adaptation_for_Multi-Label_Image_Classification_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Singh_Discriminator-Free_Unsupervised_Domain_Adaptation_for_Multi-Label_Image_Classification_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2301.10611-b31b1b.svg)](http://arxiv.org/abs/2301.10611) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=q20THPB5pBA) |
| [Few-Shot Generative Model for Skeleton-based Human Action Synthesis using Cross-Domain Adversarial Learning](https://openaccess.thecvf.com/content/WACV2024/html/Fukushi_Few-Shot_Generative_Model_for_Skeleton-Based_Human_Action_Synthesis_Using_Cross-Domain_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Fukushi_Few-Shot_Generative_Model_for_Skeleton-Based_Human_Action_Synthesis_Using_Cross-Domain_WACV_2024_paper.pdf) | :heavy_minus_sign: |
| [Mixing Gradients in Neural Networks as a Strategy to Enhance Privacy in Federated Learning](https://openaccess.thecvf.com/content/WACV2024/html/Eloul_Mixing_Gradients_in_Neural_Networks_as_a_Strategy_To_Enhance_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Eloul_Mixing_Gradients_in_Neural_Networks_as_a_Strategy_To_Enhance_WACV_2024_paper.pdf) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=DcPL-XoCc8M) |
| [Neural Style Protection: Counteracting Unauthorized Neural Style Transfer](https://openaccess.thecvf.com/content/WACV2024/html/Li_Neural_Style_Protection_Counteracting_Unauthorized_Neural_Style_Transfer_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Li_Neural_Style_Protection_Counteracting_Unauthorized_Neural_Style_Transfer_WACV_2024_paper.pdf) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=qWMKUf4ovt4) |
| [Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective](https://openaccess.thecvf.com/content/WACV2024/html/Kim_Exploring_Adversarial_Robustness_of_Vision_Transformers_in_the_Spectral_Perspective_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Kim_Exploring_Adversarial_Robustness_of_Vision_Transformers_in_the_Spectral_Perspective_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2208.09602-b31b1b.svg)](http://arxiv.org/abs/2208.09602) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=TP4MKRKGnp0) |
| [Hard-Label based Small Query Black-Box Adversarial Attack](https://openaccess.thecvf.com/content/WACV2024/html/Park_Hard-Label_Based_Small_Query_Black-Box_Adversarial_Attack_WACV_2024_paper.html) | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Park_Hard-Label_Based_Small_Query_Black-Box_Adversarial_Attack_WACV_2024_paper.pdf) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=HO7t02cK9uc) |
| [Simple Post-Training Robustness using Test Time Augmentations and Random Forest](https://openaccess.thecvf.com/content/WACV2024/html/Cohen_Simple_Post-Training_Robustness_Using_Test_Time_Augmentations_and_Random_Forest_WACV_2024_paper.html) | [![GitHub](https://img.shields.io/github/stars/giladcohen/ARF?style=flat)](https://github.com/giladcohen/ARF) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/WACV2024/papers/Cohen_Simple_Post-Training_Robustness_Using_Test_Time_Augmentations_and_Random_Forest_WACV_2024_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2109.08191-b31b1b.svg)](http://arxiv.org/abs/2109.08191) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=ElFwuIA-dwQ) |
